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CWB wrapper to extract concordances and collocates

Project description

Collocation and Concordance Computation

Introduction

This module is a wrapper around the IMS Open Corpus Workbench (CWB). Main purpose of the module is to extract concordance lines, calculate collocates, and run queries with several anchor points.

If you want to extract the results of anchored queries with more than two anchor points, the module requires CWB version 3.4.16 or newer.

Installation

You can install this module with pip from PyPI:

pip3 install cwb-ccc

You can also clone the repository from gitlab.cs.fau.de and use setup.py:

python3 setup.py install

You can also just install all requirements specified in setup.py and make sure the ccc subfolder can be found by Python by including it in your PYTHONPATH.

Usage

CWBEngine

All methods rely on the CWBEngine from ccc.cwb, which you first have to initialize with your system specific settings:

from ccc.cwb import CWBEngine
engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS",
	registry_path="/path/to/your/cwb/registry"
)

This will raise a KeyError if the named corpus is not in the specified registry.

You can use the cqp_bin to point the engine to a specific version of cqp (this is also helpful if cqp is not in your PATH) and change the cache_path:

engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS",
	registry_path="/path/to/your/cwb/registry", 
	cqp_bin="/usr/local/cwb-3.4.16/bin/cqp",
	cache_path="/tmp/ccc-cache"
)

If you are using macros and wordlists, you have to store them in a separate folder (with subfolders wordlists and macros). Make sure you specify this folder via lib_path when initializing the engine:

engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS", 
	registry_path="/path/to/your/cwb/registry",
	lib_path="/path/to/your/lib/"
)

Concordancing

You can use the Concordance class from ccc.concordances for concordancing. The concordancer has to be initialized with the engine and accepts valid CQP queries:

from ccc.concordances import Concordance
# initialize the concordancer with the engine
concordance = Concordance(engine)
# extract concordance lines
concordance.query('[lemma="Angela"] [lemma="Merkel"]')

The result will be a dictionary with the cpos of the match as keys and the entries one concordance line each. Each concordance line is formatted as a pandas.DataFrame with the cpos of each token as index:

cpos word match offset
188530363 , False -5
188530364 dass False -4
188530365 die False -3
188530366 Tage False -2
188530367 von False -1
188530368 Angela True 0
188530369 Merkel True 0
188530370 gezählt False 1
188530371 sind False 2
188530372 . False 3

The queries must not end on a "within" clause. If you want to restrict your concordance lines by a structural attribute, use the s_break parameter (defaults to "text"). The default context window is 20 tokens to the left and 20 tokens to the right of the query match and matchend, respectively.

concordance = Concordance(engine, context=50, s_break='s')
concordance.query('[lemma="Angela"] [lemma="Merkel"]')

Further parameters for the Concordance class are order (one of "random", "first", or "last"), cut_off (for the number of concordance lines to extract), and p_show (a list of additional p-attributes besides the primary word layer to show, e.g. "lemma" or "pos"; these will be added as additional columns).

Collocation Analyses

You can use the Collocates class to extract collocates for a given window size (symmetric windows around the corpus matches):

from ccc.collocates import Collocates
# initialize the collocation calculator with the engine
collocates = Collocates(engine)
# extract collocates
collocates.query('[lemma="Angela"] [lemma="Merkel"]', window=5)

The result will be a DataFrame with lexical items (lemmas by default) as index and frequency signatures and association measures as columns:

item O11 f2 N f1 O12 O21 O22 E11 E12 E21 E22 log_likelihood ...
die 1799 25125329 300917702 22832 21033 25123530 275771340 1906.373430 20925.626570 2.512342e+07 2.757714e+08 -2.459194 ...
Bundeskanzlerin 1491 8816 300917702 22832 21341 7325 300887545 0.668910 22831.331090 8.815331e+03 3.008861e+08 1822.211827 ...
. 1123 13677811 300917702 22832 21709 13676688 287218182 1037.797972 21794.202028 1.367677e+07 2.872181e+08 2.644804 ...
, 814 17562059 300917702 22832 22018 17561245 283333625 1332.513602 21499.486398 1.756073e+07 2.833341e+08 -14.204447 ...
Kanzlerin 648 17622 300917702 22832 22184 16974 300877896 1.337062 22830.662938 1.762066e+04 3.008772e+08 559.245198 ...

For notation and further information, see collocations.de.

By default, the dataframe is sorted by co-occurrence frequency (O11) and annotated with z-score, t-score, dice, log-likelihood, and mutual information. You can change these and further parameters when initializing the Collocates class:

collocates = Collocates(
	engine,
	max_window_size=20, 
	s_break='text',
	order='O11',
	p_query='lemma', 
	cut_off=100,
	ams=['z_score', 't_score', 'dice', 'log_likelihood', 'mutual_information']
)

Available association measures depend on their implementation in the association-measures module.

Note that the max_window_size parameter is an internal parameter that determines the result of the initial query via the CWBEngine. The result of the initial query is cached, which means that you can extract collocates for windows from 0 to max_window_size quickly.

Anchored Queries

The Concordance class detects anchored queries by default. The following query

concordance.query(
	'@0[lemma="Angela"]? @1[lemma="Merkel"] [word="\\("] @2[lemma="CDU"] [word="\\)"]'
)

will thus return DataFrames with an additional column indicating the anchor positions:

cpos word match offset anchor
298906425 auch False -5 None
298906426 das False -4 None
298906427 Handy False -3 None
298906428 von False -2 None
298906429 Kanzlerin False -1 None
298906430 Angela True 0 0
298906431 Merkel True 0 1
298906432 ( True 0 None
298906433 CDU True 0 2
298906434 ) True 0 None
298906435 sowie False 1 None
298906436 ihres False 2 None
298906437 Vorgängers False 3 None
298906438 Gerhard False 4 None
298906439 Schröder False 5 None

Argument Queries

Argument queries are anchored queries with additional information. (1) Each anchor can be modified by an offset (usually used to capture underspecified tokens near an anchor point). (2) Anchors can be mapped to external identifiers for further logical processing. (3) Anchors may be given a clear name:

anchor offset idx clear name
0 0 None None
1 -1 None None
2 0 None None
3 -1 None None

Furthermore, several anchor points can be combined to form regions, which in turn can be mapped to identifiers and be given a clear name:

start end idx clear name
0 1 "0" "person X"
2 3 "1" "person Y"

Example: Given the definition of anchors and regions above as well as suitable wordlists, the following complex query extracts corpus positions where there's some correlation between "person X" (the region from anchor 0 to anchor 1) and "person Y" (anchor 2 to 3):

query = (
	"<np> []* /ap[]* [lemma = $nouns_similarity] "
	"[]*</np> \"between\" @0:[::](<np>[pos_simple=\"D|A\"]* "
	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
	"lemma = $nouns_person_negative | "
	"lemma = $nouns_person_profession] |/region[ner])+ "
	"[]*</np>)+@1:[::] \"and\" @2:[::](<np>[pos_simple=\"D|A\"]* "
	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
	"lemma = $nouns_person_negative | "
	"lemma = $nouns_person_profession] | /region[ner])+ "
	"[]*</np>) (/region[np] | <vp>[lemma!=\"be\"]</vp> | "
	"/region[pp] |/be_ap[])* @3:[::]"
)

NB: the set of identifiers defined in the table of anchors and in the table of regions, respectively, should not overlap.

It is customary to store these queries in json query files such as the example in the repository. You can directly process these files using the process_argmin_file method from ccc.argmin:

from ccc.argmin import process_argmin_file
query_path = "tests/gold/query-example.json"
result = process_argmin_file(engine, query_path)

The result is a dict with the same keys as specified in the query file as well as an entry "result" with the following keys:

  • "nr_matches": the number of query matches in the corpus.
  • "matches": a list of concordance lines. Each concordance line contains
    • the corpus position of the match (entry "position")
    • the actual concordance line as returned from Concordance().query() (see above) converted to a dict (entry "df")
    • a mapping from the idx specified in the anchor and region tables to the tokens or token sequences, respectively (default: lemma layer) (entry "holes")
    • a reconstruction of the concordance line as a sequence of tokens (word layer) (entry "full")
  • "holes": a global list of all tokens of the entities specified in the "idx" columns (default: lemma layer).

Acknowledgements

The module relies on cwb-python, special thanks to Yannick Versley and Jorg Asmussen for the implementation. Thanks to Markus Opolka for the implementation of association-measures and for forcing me to write tests.

This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project "Reconstructing Arguments from Noisy Text", grant number 377333057, as part of the Priority Program "Robust Argumentation Machines (RATIO)" (SPP-1999).

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